Receiving results of uncertain clinical relevance from population genetic screening: systematic review & meta-synthesis of qualitative research
Bibliographic record
Abstract
Genetic screening can be hugely beneficial, yet its expansion poses clinical and ethical challenges due to results of uncertain clinical relevance (such as 'cystic fibrosis screen positive, inconclusive diagnosis'/CFSPID). This review systematically identifies, appraises, and synthesises the qualitative research on experiences of receiving results of uncertain clinical relevance from population genetic screening. Eight databases were systematically searched for original qualitative research using the SPIDER framework, and checked against inclusion criteria by the research team and an independent researcher. Nine papers were included (from USA, Canada, UK, New Zealand). PRISMA, ENTREQ, and EMERGE guidance were used to report. Quality was appraised using criteria for qualitative research. All papers focused on parental responses to uncertain results from newborn screening. Data were synthesised using meta-ethnography and first- and second-order constructs. Findings suggest that results of uncertain clinical relevance are often experienced in the same way as a 'full-blown' diagnosis. This has significant emotional and behavioural impact, for example adoption of lifestyle-altering disease-focused behaviours. Analysis suggests this may be due to the results not fitting a common medical model, leading recipients to interpret the significance of the result maladaptively. Findings suggest scope for professionals to negotiate and reframe uncertain screening results. Clearer initial communication is needed to reassure recipients there is no immediate severe health risk from these types of results. Public understanding of an appropriate medical model, that accounts for uncertain genetic screening results in a non-threatening way, may be key to maximising the benefits of genomic medicine and minimising potential psychological harm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.110 | 0.311 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".